کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
7259001 1472488 2018 13 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Combining ecological momentary assessment with objective, ambulatory measures of behavior and physiology in substance-use research
ترجمه فارسی عنوان
ترکیبی از ارزیابی لحظه ای اکولوژیکی با اهداف، ابعاد غیرفعال رفتار و فیزیولوژی در پژوهش های مواد مصرفی
موضوعات مرتبط
علوم زیستی و بیوفناوری علم عصب شناسی علوم اعصاب رفتاری
چکیده انگلیسی
Whereas substance-use researchers have long combined self-report with objective measures of behavior and physiology inside the laboratory, developments in mobile/wearable electronic technology are increasingly allowing for the collection of both subjective and objective information in participants' daily lives. For self-report, ecological momentary assessment (EMA), as implemented on contemporary smartphones or personal digital assistants, can provide researchers with near-real-time information on participants' behavior and mood in their natural environments. Data from portable/wearable electronic sensors measuring participants' internal and external environments can be combined with EMA (e.g., by timestamps recorded on questionnaires) to provide objective information useful in determining the momentary context of behavior and mood and/or validating participants' self-reports. Here, we review three objective ambulatory monitoring techniques that have been combined with EMA, with a focus on detecting drug use and/or measuring the behavioral or physiological correlates of mental events (i.e., emotions, cognitions): (1) collection and processing of biological samples in the field to measure drug use or participants' physiological activity (e.g., hypothalamic-pituitary-adrenal axis activity); (2) global positioning system (GPS) location information to link environmental characteristics (disorder/disadvantage, retail drug outlets) to drug use and affect; (3) ambulatory electronic physiological monitoring (e.g., electrocardiography) to detect drug use and mental events, as advances in machine learning algorithms make it possible to distinguish target changes from confounds (e.g., physical activity). Finally, we consider several other mobile/wearable technologies that hold promise to be combined with EMA, as well as potential challenges faced by researchers working with multiple mobile/wearable technologies simultaneously in the field.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Addictive Behaviors - Volume 83, August 2018, Pages 5-17
نویسندگان
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